Yinxiao Liu

Adult bone diseases, especially osteoporosis, lead to increased risk of fracture which in turn is associated with substantial morbidity, mortality, and financial costs. Clinically, osteoporosis is defined by low bone mineral density; however, increasing evidence suggests that the microarchitectural quality of trabecular bone (TB) is an important determinant of bone strength and fracture risk. Accurate measures of TB thickness and marrow spacing is of significant interest for early diagnosis of osteoporosis or treatment effects. Here, we present a new robust algorithm for computing TB thickness and marrow spacing at a low resolution achievable in vivo . The method uses a star-line tracing technique that effectively deals with partial voluming effects of in vivo imaging with voxel size comparable to TB thickness. Also, the method avoids the problem of digitization associated with conventional algorithms based on sampling distance transform along skeletons. Accuracy of the method was examined using computer-generated phantom images, while the robustness of the method was evaluated on human ankle specimens in terms of stability across a wide range of voxel sizes, repeat scan reproducibility under in vivo conditions, and correlation between thickness values computed at ex vivo and in vivo imaging resolutions. Also, the sensitivity of the method was examined by evaluating its ability to predict the bone strength of cadaveric specimens. Finally, the method was evaluated in a human study involving 40 healthy young-adult volunteers (age: 19–21 years; 20 males and 20 females) and ten athletes (age: 19–21 years; six males and four females). Across a wide range of voxel sizes, the new method is significantly more accurate and robust as compared to conventional methods. Both TB thickness and marrow spacing measures computed using the new method demonstrated strong associations ( $R^2\in [0.83,0.87]$ ) with bone strength. Also, the TB thickness and marrow spacing measures allowed discrimination between male and female volunteers ( $p\in [0.01,0.04]$ ) as well as between athletes and nonathletes ( $p\in [0.005,0.03]$ ).

Purpose: Image thresholding and gradient analysis have remained popular image preprocessing tools for several decades due to the simplicity and straight-forwardness of their definitions. Also, optimum selection of threshold and gradient strength values are hidden steps in many advanced medical imaging algorithms. A reliable method for threshold optimization may be a crucial step toward automation of several medical image based applications. Most automatic thresholding and gradient selection methods reported in literature primarily focus on image histograms ignoring a significant amount of information embedded in the spatial distribution of intensity values forming visible features in an image. Here, we present a new method that simultaneously optimizes both threshold and gradient values for different object interfaces in an image that is based on unification of information from both the histogram and spatialimage features; also, the method works for unknown number of object regions. Methods: A new energy function is formulated by combining the object class uncertainty measure, a histogram-based feature, of each pixel with its image gradient measure, a spatial contextual feature in an image. The energy function is designed to measure the overall compliance of the theoretical premise that, in a probabilistic sense, image intensities with high class uncertainty are associated with high image gradients. Finally, it is expressed as a function of threshold and gradient parameters and optimum combinations of these parameters are sought by locating pits and valleys on the energy surface. A major strength of the algorithm lies in the fact that it does not require the number of object regions in an image to be predefined. Results: The method has been applied on several medical image datasets and it has successfully determined both threshold and gradient parameters for different object interfaces even when some of the thresholds are almost impossible to locate in the histogram. Both accuracy and reproducibility of the method have been examined on several medical image datasets including repeat scan 3D multidetector computed tomography(CT)images of cadaveric ankles specimens. Also, the new method has been qualitatively and quantitatively compared with Otsu’s method along with three other algorithms based on minimum error thresholding, maximum segmented image information and minimization of homogeneity- and uncertainty-based energy and the results have demonstrated superiority of the new method. Conclusions: We have developed a new automatic threshold and gradient strength selection algorithm by combining class uncertainty and spatialimage gradient features. The performance of the method has been examined in terms of accuracy and reproducibility and the results found are better as compared to several popular automatic threshold selection methods.

Background The impact of comorbidities on outcomes of patients with lower gastrointestinal bleeding (LGIB) remains unknown. Objective Investigate the prevalence of comorbidities and impact on outcomes of patients with LGIB. Methods The Nationwide Inpatient Sample 2010 was used to identify patients who had a primary discharge diagnosis of LGIB based on International Classification of Diseases, the 9th revision, clinical modification codes. The presence of comorbid illness was assessed using the Elixhauser index. Logistic regression models were used to assess the contributions of the individual Elixhauser comorbidities to predict in-hospital mortality. Results A total of 58,296 discharges with LGIB were identified. The overall mortality was 2.3 %. Among the patients who underwent colonoscopy, 17.3 % of patients had therapeutic intervention. As the number of comorbidities increased (i.e., 0, 1, 2, or >3), mortality increased (1.7, 2.0, 2.4, and 2.4 %, respectively). The mortality rate was highest in patients >65 years of age (2.7 %). Patients >65 years of age with two or more comorbidities had a mortality rate of 5 % as compared to 2.6 % in those with less than two comorbidities. Congestive heart failure (odds ratio, 1.67 [95 % confidence interval, 1.48–1.95]), liver disease (2.64 [1.83–3.80]), renal failure (1.99 [1.70–2.33]), and weight loss (2.66 [2.27–3.12]) were associated with a significant increase in mortality rate. Comorbidities increased hospital stay and costs. Conclusions Comorbidities were associated with increased the risk of mortality and health care utilization in patients with LGIB. Identification of comorbidities and development of risk-adjustment tools may improve the outcome of patients with LGIB.